Ionospheric delay estimation model-based long baseline monitoring method, device and medium

Abstract

This invention discloses a method, apparatus, and medium for medium- and long-baseline monitoring based on an ionospheric delay estimation model. The method includes: attaching an ionospheric parameter to each satellite; constructing an ionospheric delay time constraint model using a random walk model; obtaining narrow-lane floating-point ambiguity through a Kalman-filtered narrow-lane double-difference observation model and wide-lane floating-point ambiguity through a filtered wide-lane double-difference observation model; fixing the wide-lane fixed ambiguity through a LAMBDA search, selecting a high-precision constrained narrow-lane floating-point ambiguity, and then fixing the narrow-lane fixed ambiguity again through a LAMBDA search; verifying the narrow-lane fixed ambiguity and updating the position parameters. This invention fully considers the variability and time correlation of ionospheric delay, improving the quality and accuracy of medium- and long-baseline calculations. Furthermore, selecting highly reliable wide-lane ambiguities as observation values ​​to constrain the narrow-lane floating-point ambiguities accelerates the convergence of the floating-point ambiguities and the ionospheric delay parameter.

Description

Technical Field

[0001] This invention relates to the field of GNSS positioning technology, and specifically to a medium- and long-baseline monitoring method, device, and medium based on an ionospheric delay estimation model. Background Technology

[0002] After nearly a decade of development, GNSS relative positioning technology has gradually matured in the field of deformation monitoring. Due to its high precision, automation, and all-weather capabilities, GNSS relative positioning monitoring technology has been widely applied in scenarios such as slope protection, building construction, long-span bridges, tailings ponds, and dams and reservoirs, making significant contributions to ensuring the safety of people's property and health.

[0003] Currently, the short baseline (less than 10km) relative positioning mode is mainly used in monitoring scenarios. However, the application of the short baseline mode requires the construction of a large number of base stations, resulting in a waste of resources. Therefore, GNSS relative positioning technology suitable for medium and long baseline (10-30km) monitoring scenarios is particularly important.

[0004] Currently, GNSS solutions based on long baselines have the following drawbacks:

[0005] First, because there is no strong relationship between the ionospheric delays of each satellite, the existing scheme does not take into account the differences and time correlations of the ionospheric delays of each satellite, resulting in insufficient calculation accuracy.

[0006] Second, the convergence speed of floating-point ambiguity is slow, and its real-time performance is not high.

[0007] Third, the method for checking the accuracy of ambiguity is too simplistic and prone to misjudgment and omission.

[0008] Therefore, it is necessary to improve the existing methods for resolving ambiguity in medium and long baselines in order to improve the accuracy of the solution results and increase the convergence speed of ambiguity. Summary of the Invention

[0009] To address the aforementioned shortcomings, the technical problem to be solved by this invention is to provide a long baseline monitoring method, device, and medium based on an ionospheric delay estimation model, so as to solve the problem that the existing technology does not consider the differences and time correlations of the ionospheric delay of each satellite, resulting in insufficient solution accuracy and slow convergence speed of ambiguity.

[0010] Therefore, the present invention provides a long baseline monitoring method based on an ionospheric delay estimation model, comprising the following steps:

[0011] An ionospheric parameter is added to each satellite to construct a narrow-lane double-difference observation model consisting of position parameters, double-difference ambiguity, relative zenith tropospheric delay, and ionospheric delay;

[0012] An ionospheric delay time constraint model for the above-mentioned narrow alley double-difference observation model is constructed using a random walk model;

[0013] The narrow-lane double-difference observation model is filtered by the ionospheric delay time constraint model based on the narrow-lane double-difference observation model to obtain the first floating-point position and the narrow-lane floating-point ambiguity.

[0014] A wide-lane double-difference observation model is constructed, consisting of location parameters, double-difference wide-lane ambiguity, and relative zenith tropospheric delay.

[0015] The wide-lane double-difference observation model is filtered by a wide-lane Kalman filter to obtain the second floating-point position and wide-lane floating-point ambiguity, and the wide-lane fixed ambiguity is obtained by LAMBDA search.

[0016] We select the wide-lane fixed ambiguity with higher accuracy as the observation value and construct the constrained observation equation between the wide-lane fixed ambiguity and the narrow-lane floating-point ambiguity. in, To fix the ambiguity of the wide alley, and , respectively, represent the narrow-lane floating-point ambiguity at frequency L1 and frequency L2, and e represents the rounding error of the narrow-lane floating-point ambiguity; the selection criterion for the wide-lane fixed ambiguity with higher accuracy is that the difference between its fixed solution and floating-point solution is less than 0.2 cycles;

[0017] By searching for fixed narrow alley floating-point ambiguities using LAMBDA, at least some fixed narrow alley ambiguities can be obtained.

[0018] Perform fuzziness accuracy checks on fixed fuzziness in narrow alleyways;

[0019] The location parameters are updated based on the fixed ambiguity of the narrow alley after verification, and the real-time location coordinates are obtained.

[0020] In the above method, preferably, the following three methods are combined to perform the correctness check of narrow alley fixed ambiguity;

[0021] Method 1: Perform an iterative chi-square test on the fixed single-frequency ambiguity residuals in the narrow alley fixed ambiguity and remove single-frequency ambiguities that do not conform to the chi-square distribution;

[0022] Method 2: Combine the fixed dual-frequency ambiguities in the narrow alley fixed ambiguity and calculate to obtain the wide alley ambiguity, compare it with the fixed wide alley ambiguity, and remove inconsistent dual-frequency ambiguities;

[0023] Method 3: Calculate the residuals v1 and v2 of the fixed dual-frequency ambiguities in the narrow alley fixed ambiguity, and filter the dual-frequency ambiguities according to the following conditions.

[0024] In the above method, preferably, the narrow alley double-difference observation model is expressed as:

[0025] y = B·b + A·a + D·t + F·i + ε;

[0026] After further merging, we get: y = H·x + v;

[0027] Where y represents the observation term, b and B are the position parameter term and their coefficient matrix, a and A are the ambiguity parameter term and their coefficient matrix, t and D are the tropospheric delay and their coefficient matrix, i and F are the ionospheric delay and their coefficient matrix forming the ionospheric parameter, ε represents the error, and x = [btia]. T , where represents the parameter matrix, H is the coefficient matrix after merging the parameters, and v represents the error matrix.

[0028] In the above method, preferably,

[0029] The narrow-lane double-difference observation model is in matrix form of the carrier-phase double-difference observation equation, which is:

[0030]

[0031] In the formula, λ f For carrier wavelength, For double-difference phase observations, Δr and These are the position corrections to be estimated and their coefficients, respectively. γ and θ are the mapping functions of the double-difference zenith tropospheric delay and its related elevation angle, respectively. and denoted by f, f1 represents the ionospheric delay of the single-difference slant path for satellites i and j, respectively; f is the frequency of the current frequency point; and f1 is the frequency of the L1 frequency point. For double-difference ambiguity, This represents the phase residual.

[0032] In the above method, preferably, the ionospheric delay time constraint model is as follows:

[0033] I k =I k-1 +ΔI+ε I ,ε I ∈(0,D I );

[0034] Where ΔI is the epochal variation of ionospheric delay, and D I The constraint variance for ionospheric variation is set to 1–4 cm / s, ε I This indicates the error in ionospheric variation.

[0035] In the above method, preferably, the wide-lane double-difference observation model is expressed as:

[0036] y w =H w ·x w +v w ;

[0037] Among them, y w For the observed value term, H w The coefficient matrix after merging parameters, v w Let x represent the error matrix. w =[b w t w a w ] T b w Let a be the coefficient matrix of the position parameter terms. w Let t be the coefficient matrix of the wide-lane ambiguity. w This is the coefficient matrix for tropospheric delay;

[0038] The wide-lane double-difference observation model is in matrix form of the wide-lane double-difference observation equation, which is as follows:

[0039]

[0040] in,

[0041] In the above method, preferably, after obtaining the narrow alley floating-point ambiguity and its variance matrix, when obtaining the narrow alley fixed ambiguity through LAMBDA search, if it is not possible to fix all of them, then partial fixing is performed based on the ADOP minimum principle. First, single-frequency ambiguities are eliminated, then dual-frequency ambiguities are eliminated, until the ratio value is greater than 2.5.

[0042] The present invention also provides a long baseline monitoring device based on an ionospheric delay estimation model, comprising:

[0043] The narrow-lane double-difference observation model consists of position parameters, double-difference ambiguity, relative zenith tropospheric delay, and ionospheric delay, with an additional ionospheric parameter on each satellite.

[0044] The ionospheric delay time constraint model is constructed using a random walk model to build the ionospheric delay time constraint model of the above-mentioned narrow alley double-difference observation model;

[0045] The narrow-lane Kalman filter is based on the ionospheric delay time constraint model of the narrow-lane double-difference observation model to filter the narrow-lane double-difference observation model and obtain the first floating-point position and the narrow-lane floating-point ambiguity.

[0046] The wide-lane double-difference observation model consists of location parameters, double-difference wide-lane ambiguity, and relative zenith tropospheric delay.

[0047] A wide-lane Kalman filter is used to filter the wide-lane double-difference observation model to obtain the second floating-point position and wide-lane floating-point ambiguity, and the wide-lane fixed ambiguity is obtained by LAMBDA search.

[0048] The search module is used to obtain the fixed ambiguity of the wide lane using LAMBDA, and selects the wide lane fixed ambiguity with higher accuracy as the observation value to construct the constrained observation equation between the wide lane fixed ambiguity and the narrow lane floating-point ambiguity. in, To fix the ambiguity of the wide alley, and The floating-point ambiguities of the narrow lanes at frequencies L1 and L2 are respectively, and e is the rounding error of the floating-point ambiguity. The selection criterion for the wide lane fixed ambiguity with higher accuracy is that the difference between its fixed solution and the floating-point solution is less than 0.2 cycles. After the narrow lane floating-point ambiguity is constrained and corrected, at least a portion of the narrow lane fixed ambiguity is obtained by searching for fixed ambiguity.

[0049] The ambiguity correctness check module is used to check the fixed ambiguity of the narrow alleyway after it has been fixed.

[0050] The location update module is used to update the location parameters based on the fixed ambiguity of the narrow alley after verification, so as to obtain the real-time location coordinates.

[0051] In the above system, preferably, the following three methods are combined to perform narrow alleyway ambiguity correctness verification;

[0052] Method 1: Perform an iterative chi-square test on the fixed single-frequency ambiguity residuals in the narrow alley fixed ambiguity and remove single-frequency ambiguities that do not conform to the chi-square distribution;

[0053] Method 2: Combine the fixed dual-frequency ambiguities in the narrow alley fixed ambiguity and calculate to obtain the wide alley ambiguity, compare it with the fixed wide alley ambiguity, and remove inconsistent dual-frequency ambiguities;

[0054] Method 3: Calculate the residuals v1 and v2 of the fixed dual-frequency ambiguities in the narrow alley fixed ambiguity, and filter the dual-frequency ambiguities according to the following conditions.

[0055] The present invention also provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the above-described medium- and long-baseline monitoring method based on an ionospheric delay estimation model.

[0056] As can be seen from the above technical solution, the medium-to-long baseline monitoring method, device, and medium based on an ionospheric delay estimation model provided by this invention solves the problems of insufficient solution accuracy and slow convergence speed of floating-point ambiguity caused by the prior art's failure to consider the differences and time correlations of ionospheric delay for each satellite. Compared with the prior art, this invention has the following beneficial effects:

[0057] An ionospheric relative delay estimator was constructed, with an ionospheric parameter set for each satellite. A random walk model was used to impose time constraints on the relative ionospheric delay, fully considering the variability and temporal correlation of ionospheric delay, thus improving the quality and accuracy of medium- and long-baseline solutions. Furthermore, a more easily fixed wide-lane floating-point ambiguity combination and its filter were developed. By constructing a constraint observation equation between the wide-lane fixed ambiguity and the narrow-lane floating-point ambiguity, the convergence of floating-point ambiguity and ionospheric delay parameters was accelerated. Attached Figure Description

[0058] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments of the present invention or the prior art will be briefly introduced and explained below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 The present invention provides a flowchart of a long baseline monitoring method based on an ionospheric delay estimation model. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] To provide a clearer explanation and description of the technical solution and implementation of the present invention, several preferred specific embodiments for implementing the technical solution of the present invention are described below.

[0062] It should be noted that the directional terms such as "inner" and "outer", "front" and "back" and "left" and "right" in this article are based on the product's usage status. Obviously, the use of these directional terms does not limit the scope of protection of this solution.

[0063] Currently, ionospheric errors in observations can be eliminated by constructing an anti-ionospheric combination using dual-frequency observations. The anti-ionospheric combination form of its carrier phase can be expressed as follows:

[0064]

[0065] f1 and f2 are the frequencies of the dual-frequency receiver, φ1 and φ2 are the phases, and λ1 and λ2 are the wavelengths.

[0066] wavelength of combined observations The ambiguity N after combination is:

[0067] N=f1N1-f2N2=f1(N1-N2)+(f1-f2)N2.

[0068] Therefore, the combined ambiguity can be decomposed into a wide-lane floating-point ambiguity term N2 and a narrow-lane floating-point ambiguity term N1. The wide-lane floating-point ambiguity N2 can be fixed first, followed by the narrow-lane floating-point ambiguity N1 for solution. Although this combination achieves the goal of eliminating ionospheric delay, it also objectively amplifies observation noise, resulting in greater fluctuations in the position solution compared to traditional short-baseline RTK.

[0069] To address this, this invention constructs a relative ionospheric delay estimator. Each satellite is assigned an ionospheric parameter. Narrow-lane floating-point ambiguity is obtained using a Kalman-filtered narrow-lane double-difference observation model, and wide-lane floating-point ambiguity is obtained using a filtered wide-lane double-difference observation model. Wide-lane fixed ambiguity is obtained through LAMBDA search, and a high-precision constrained narrow-lane floating-point ambiguity is selected, then fixed through LAMBDA search again to obtain narrow-lane fixed ambiguity. The narrow-lane fixed ambiguity is checked, and the position parameters are updated. A random walk model is used to impose time constraints on the corresponding relative ionospheric delay, fully considering the differences in ionospheric delays between different satellites and the time correlation of ionospheric delays within the same satellite. This reduces RTK fluctuations and improves the solution quality and accuracy for medium- and long-baseline systems.

[0070] Figure 1 This invention provides a flowchart of a medium-to-long baseline monitoring method based on an ionospheric delay estimation model. Figure 1 As shown, the method includes the following steps:

[0071] Step 110: Add an ionospheric parameter to each satellite to construct a narrow-lane double-difference observation model consisting of position parameters, double-difference ambiguity, relative zenith tropospheric delay, and ionospheric delay, represented as:

[0072] y = B·b + A·a + D·t + F·i + ε.

[0073] Where y is the observation term, b and B are the position parameter terms and their coefficient matrices, a and A are the ambiguity parameter terms and their coefficient matrices, t and D are the tropospheric delay and their coefficient matrices, i and F are the ionospheric parameters composed of the ionospheric delay and their coefficient matrices, and ε represents the error.

[0074] After further merging, we get:

[0075] y = H·x + v.

[0076] Where x = [btia] T H is the coefficient matrix after merging parameters, and v represents the error matrix.

[0077] The narrow-lane double-difference observation model is in matrix form of the carrier-phase double-difference observation equation, which is expressed as:

[0078]

[0079] In the formula, λ f The carrier wavelength (m) For the double-difference phase observation (m), Δr and These are the position correction (m) to be estimated and its coefficient term, respectively. γ(θ) and γ(θ) are mapping functions of the double-difference zenith tropospheric delay (m) and its related elevation angle, respectively. and Let f be the ionospheric delay (m) of the single-difference slant path for satellites i and j, respectively, where f is the frequency of the current frequency point and f1 is the frequency of the L1 frequency point. For double-difference ambiguity (cycle, period), This represents the phase residual (m). The subscript br indicates "baseband receiver".

[0080] Step 120: Construct the ionospheric delay time constraint model I of the above-mentioned narrow-lane double-difference observation model using a random walk model. k .

[0081] I k =I k-1 +ΔI+ε I ,ε I ∈(0,D I ).

[0082] Where k represents the current epoch, k-1 represents the previous epoch, ΔI is the epochal change in ionospheric delay, and D I The constraint variance for ionospheric variation can be set to 1–4 cm / s, ε I This represents the ionospheric delay error.

[0083] Step 130: Construct a narrow-lane Kalman filter. Based on the ionospheric delay time constraint model of the narrow-lane double-difference observation model, filter the narrow-lane double-difference observation model to obtain the first floating-point position and the narrow-lane floating-point ambiguity.

[0084] The Kalman filter algorithm formula is as follows:

[0085] (1) Forecast update:

[0086] This indicates that the state estimate at the current time k is predicted based on the state estimate at the previous time k-1.

[0087] P k|k-1 =P k-1 This indicates that the state covariance estimate at the current time k is obtained by predicting the state covariance estimate at the previous time k-1.

[0088] (2) Measurement update:

[0089] Kalman gain matrix K k =P k|k-1 HP k|k-1 H T +R) -1 Among them, P k|k-1 It is the state covariance estimate at the current time k in the prediction step, H is the observation matrix, and R is the observation noise covariance matrix.

[0090] Kalman filter optimal estimate This indicates that based on the latest measured value y k And previous estimates Update and estimate the system state. This represents the residual, which is the difference between the predicted and actual measured values.

[0091] Optimal estimate of covariance P k|k =(IK k H)P k|k-1 This indicates that the optimal estimate of the covariance P from the previous step is used. k|k-1 and Kalman gain matrix K k Update to the current optimal estimate of covariance P k|k Among them, IK k H represents the effect of the Kalman gain, which determines how much weight is allocated to the previously estimated covariance P. k|k-1 .

[0092] Step 140: Construct a wide-lane double-difference observation model consisting of double-difference wide-lane observations and relative zenith tropospheric delay, expressed as:

[0093] y w =H w·x w +v w .

[0094] Among them, y w For the observed value term, H w The coefficient matrix after merging parameters, v w Let x represent the error matrix. w =[b w t w a w ] T b w Let a be the coefficient matrix of the position parameter terms. w Let t be the coefficient matrix of the wide-lane ambiguity. w This is the coefficient matrix of tropospheric delay.

[0095] The wide-lane double-difference observation model is in matrix form of the wide-lane double-difference observation equation. The wide-lane double-difference observation equation is similar to that of the narrow-lane double-difference observation model, as follows:

[0096]

[0097] in, The subscript w indicates the width of the alley.

[0098] Because the tropospheric delay error of the double-difference wide-lane observation is comparable to that of the L1 frequency, and the ionospheric delay error is only 1.28 times that of L1, and the wavelength of the wide-lane combination can reach 86 cm, which is more than 4 times larger than that of L1, the influence of the wide-lane ionosphere on the wide-lane residual is about 0.28 times that of the narrow-lane ionosphere on the L1 frequency. The influence of ionospheric delay can be ignored, and only one zenith tropospheric delay parameter is added.

[0099] Step 150: Use a broadband Kalman filter to filter the wide-lane double-difference observation model to obtain the second floating-point position and the wide-lane floating-point ambiguity, and use LAMBDA search to fix the wide-lane fixed ambiguity.

[0100] Step 160: Among the fixed ambiguities of the wide alley, select the wide alley fixed floating-point ambiguity with higher accuracy as the observation value, and construct the constrained observation equation between the wide alley fixed ambiguity and the narrow alley floating-point ambiguity. in, To fix the ambiguity of the wide alley, and Here, represents the narrow-lane floating-point ambiguity at frequency L1 and L2, respectively, and e represents the rounding error of the narrow-lane floating-point ambiguity. The selection criterion for high-precision wide-lane fixed ambiguity is that the difference between its fixed solution and floating-point solution is less than 0.2 cycles. After the floating-point ambiguity in the narrow alley is constrained and corrected, at least a portion of the narrow alley fixed ambiguity is obtained through LAMBDA search, which further promotes the convergence of various filtering parameters in the narrow alley double-difference observation model.

[0101] In the process of searching for fixed narrow alley fixed ambiguities, if it is not possible to fix all of them, partial fixing is performed based on the ADOP minimum principle. First, single-frequency ambiguities are eliminated, then dual-frequency ambiguities are eliminated, until the ratio value is greater than 2.5.

[0102] Step 170: Use the following three methods in combination to check the correctness of the fixed ambiguity in narrow alleyways;

[0103] Method 1: Perform an iterative chi-square test on the fixed single-frequency ambiguity residuals in the narrow alley fixed ambiguity and remove single-frequency ambiguities that do not conform to the chi-square distribution;

[0104] Method 2: Combine the fixed dual-frequency ambiguities in the narrow alley fixed ambiguity and calculate to obtain the wide alley ambiguity, compare it with the fixed wide alley ambiguity, and remove inconsistent dual-frequency ambiguities;

[0105] Method 3: Calculate the residuals v1 and v2 of the fixed dual-frequency ambiguities in the narrow alley fixed ambiguity, and filter the dual-frequency ambiguities according to the following conditions.

[0106] Assuming that the relative zenith tropospheric delay converges after long-term filtering, but since the ionospheric parameters of each satellite are estimated individually, it is impossible to maintain a convergent state indefinitely. Therefore, in addition to non-model errors, residual ionospheric delay errors exist in v1 and v2. This error is essentially the difference between the unconverged ionospheric parameters and the true ionospheric delay, following a certain order. Taking the upper bound value 2ε1 < 0.04m, the ambiguity correctness check condition is:

[0107]

[0108] Step 180: Update the position parameters based on the fixed ambiguity of the narrow alley after verification to obtain the real-time position coordinates.

[0109]

[0110] in, This represents the estimated value of the updated position coordinate parameters;

[0111] This represents an estimated value of the original position coordinate parameters;

[0112] Represents position parameters With ambiguity parameter The covariance matrix between them;

[0113] The inverse matrix representing the covariance matrix of the ambiguity parameters;

[0114] Represents ambiguity parameters With position parameters The covariance matrix between them;

[0115] This represents the original estimated value of the ambiguity parameter;

[0116] This represents the updated estimate of the ambiguity parameter.

[0117] Based on the above method, the present invention also provides a medium-to-long baseline monitoring device based on an ionospheric delay estimation model, comprising:

[0118] The narrow-lane double-difference observation model consists of position parameters, double-difference ambiguity, relative zenith tropospheric delay, and ionospheric delay, with an additional ionospheric parameter on each satellite.

[0119] The ionospheric delay time constraint model is constructed using a random walk model to build the ionospheric delay time constraint model of the above-mentioned narrow alley double-difference observation model;

[0120] The narrow-lane Kalman filter is based on the ionospheric delay time constraint model of the narrow-lane double-difference observation model to filter the narrow-lane double-difference observation model and obtain the first floating-point position and the narrow-lane floating-point ambiguity.

[0121] The wide-lane double-difference observation model consists of location parameters, double-difference wide-lane ambiguity, and relative zenith tropospheric delay.

[0122] A wide-lane Kalman filter is used to filter the wide-lane double-difference observation model to obtain the second floating-point position and wide-lane floating-point ambiguity, and the wide-lane fixed ambiguity is obtained by LAMBDA search.

[0123] The search module is used to obtain the fixed ambiguity of the wide lane using LAMBDA, and selects the wide lane fixed ambiguity with higher accuracy as the observation value to construct the constrained observation equation between the wide lane fixed ambiguity and the narrow lane floating-point ambiguity. in, To fix the ambiguity of the wide alley, and The floating-point ambiguities of the narrow lanes at frequencies L1 and L2 are respectively, and e is the rounding error of the floating-point ambiguity. The selection criterion for the wide lane fixed ambiguity with higher accuracy is that the difference between its fixed solution and the floating-point solution is less than 0.2 cycles. After the narrow lane floating-point ambiguity is constrained and corrected, at least a portion of the narrow lane fixed ambiguity is obtained by searching for fixed ambiguity.

[0124] The ambiguity correctness check module is used to check the fixed ambiguity of the narrow alleyway after it has been fixed.

[0125] The location update module is used to update the location parameters based on the fixed ambiguity of the narrow alley after verification, so as to obtain the real-time location coordinates.

[0126] The long baseline monitoring method based on the ionospheric delay estimation model in this invention can be implemented as a computer software program. For example, this invention also provides a computer-readable medium storing a computer program that, when executed by a processor, implements the aforementioned long baseline monitoring method based on the ionospheric delay estimation model.

[0127] Based on the above description of specific embodiments, the medium-to-long baseline monitoring method, device, and medium based on the ionospheric delay estimation model provided by the present invention have the following advantages compared with the prior art:

[0128] First, an ionospheric parameter is set for each satellite. By constructing an ionospheric relative delay estimator and using a random walk model to constrain the relative ionospheric delay over time, the differences and time correlations of ionospheric delay are fully considered, thus improving the quality and accuracy of medium- and long-baseline solutions.

[0129] Second, taking advantage of the ease of fixing wide-lane ambiguity, the wide-lane ambiguity is fixed first. Then, a high-precision fixed wide-lane ambiguity is selected as the observation value to form a constraint equation with the narrow-lane floating-point ambiguity to improve the accuracy of the narrow-lane floating-point ambiguity. The narrow-lane floating-point ambiguity is then searched and fixed using LAMBDA. By constructing observation equations that constrain the wide-lane floating-point ambiguity and the narrow-lane floating-point ambiguity, a combination of wide-lane floating-point ambiguities and its filter that is easier to fix is ​​constructed. From this, a high-reliability wide-lane floating-point ambiguity is selected to constrain the narrow-lane floating-point ambiguity, thereby accelerating the convergence of the floating-point ambiguity and the ionospheric delay parameter.

[0130] Third, the reliability of the fixed ambiguity of the narrow lane is improved by checking the fixed ambiguity through three methods: single-frequency ambiguity residual chi-square test, dual-frequency ambiguity wide lane test, and dual-frequency ambiguity residual ionospheric characteristic test.

[0131] Finally, it should be noted that the terms "comprising," "including," or any other variations thereof as used herein are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0132] This invention is not limited to the above-described preferred embodiments. Anyone should know that any structural changes made under the guidance of this invention, and any technical solutions that are the same as or similar to this invention, fall within the protection scope of this invention.

Claims

1. A long baseline monitoring method based on an ionospheric delay estimation model, characterized in that, Includes the following steps: An ionospheric parameter is added to each satellite to construct a narrow-lane double-difference observation model consisting of position parameters, double-difference ambiguity, relative zenith tropospheric delay, and ionospheric delay; An ionospheric delay time constraint model for the above-mentioned narrow alley double-difference observation model is constructed using a random walk model; The narrow-lane double-difference observation model is filtered by the ionospheric delay time constraint model based on the narrow-lane double-difference observation model to obtain the first floating-point position and the narrow-lane floating-point ambiguity. A wide-lane double-difference observation model is constructed, consisting of location parameters, double-difference wide-lane ambiguity, and relative zenith tropospheric delay. The wide-lane double-difference observation model is filtered by a wide-lane Kalman filter to obtain the second floating-point position and wide-lane floating-point ambiguity, and the wide-lane fixed ambiguity is obtained by LAMBDA search. We select the wide-lane fixed ambiguity with higher accuracy as the observation value and construct the constrained observation equation between the wide-lane fixed ambiguity and the narrow-lane floating-point ambiguity. in, To fix the ambiguity of the wide alley, and , respectively, represent the narrow-lane floating-point ambiguity at frequency L1 and frequency L2, and e represents the rounding error of the narrow-lane floating-point ambiguity; the selection criterion for the wide-lane fixed ambiguity with higher accuracy is that the difference between its fixed solution and floating-point solution is less than 0.2 cycles; By searching for fixed narrow alley floating-point ambiguities using LAMBDA, at least some fixed narrow alley ambiguities can be obtained. Perform fuzziness accuracy checks on fixed fuzziness in narrow alleyways; The location parameters are updated based on the fixed ambiguity of the narrow alley after verification, and the real-time location coordinates are obtained.

2. The method according to claim 1, characterized in that, The following three methods are combined to check the correctness of fixed ambiguity in narrow alleyways; Method 1: Perform an iterative chi-square test on the fixed single-frequency ambiguity residuals in the narrow alley fixed ambiguity and remove single-frequency ambiguities that do not conform to the chi-square distribution; Method 2: Combine the fixed dual-frequency ambiguities in the narrow alley fixed ambiguity and calculate to obtain the wide alley ambiguity, compare it with the fixed wide alley ambiguity, and remove inconsistent dual-frequency ambiguities; Method 3: Calculate the residuals v1 and v2 of the fixed dual-frequency ambiguities in the narrow alley fixed ambiguity, and filter the dual-frequency ambiguities according to the following conditions.

3. The method according to claim 1, characterized in that, The narrow alley double-difference observation model is expressed as: y = B·b + A·a + D·t + F·i + ε; After further merging, we get: y = H·x + v; Where y represents the observation term, b and B are the position parameter term and their coefficient matrix, a and A are the ambiguity parameter term and their coefficient matrix, t and D are the tropospheric delay and their coefficient matrix, i and F are the ionospheric delay and their coefficient matrix forming the ionospheric parameter, ε represents the error, and x = [btia]. T , where represents the parameter matrix, H is the coefficient matrix after merging the parameters, and v represents the error matrix.

4. The method according to claim 3, characterized in that, The narrow-lane double-difference observation model is in matrix form of the carrier-phase double-difference observation equation, which is: In the formula, λ f For carrier wavelength, For double-difference phase observations, Δr and These are the position corrections to be estimated and their coefficients, respectively. γ and θ are the mapping functions of the double-difference zenith tropospheric delay and its related elevation angle, respectively. and denoted by f, f1 represents the ionospheric delay of the single-difference slant path for satellites i and j, respectively; f is the frequency of the current frequency point; and f1 is the frequency of the L1 frequency point. For double-difference ambiguity, This represents the phase residual.

5. The method according to claim 1, characterized in that, The ionospheric delay time constraint model is as follows: I k =I k-1 +ΔI+ε I ,ε I ∈(0,D I ); Where ΔI is the epochal variation of ionospheric delay, and D I The constraint variance for ionospheric variation is set to 1–4 cm / s, ε I This indicates the error in ionospheric variation.

6. The method according to claim 4, characterized in that, The wide-lane double-difference observation model is expressed as: y w =H w ·x w +v w ; Among them, y w For the observed value term, H w The coefficient matrix after merging parameters, v w Let x represent the error matrix. w =[b w t w a w ] T b w Let a be the coefficient matrix of the position parameter terms. w Let t be the coefficient matrix of the wide-lane ambiguity. w This is the coefficient matrix for tropospheric delay; The wide-lane double-difference observation model is in matrix form of the wide-lane double-difference observation equation, which is as follows: in, 7. The method according to claim 1, characterized in that, After obtaining the narrow alley floating-point ambiguity and its variance matrix, when fixing the narrow alley fixed ambiguity through LAMBDA search, if it is not possible to fix all of them, partial fixing is performed based on the ADOP minimum principle. First, single-frequency ambiguities are eliminated, then dual-frequency ambiguities are eliminated, until the ratio value is greater than 2.

5.

8. A long baseline monitoring device based on an ionospheric delay estimation model, characterized in that, include: The narrow-lane double-difference observation model consists of position parameters, double-difference ambiguity, relative zenith tropospheric delay, and ionospheric delay, with an additional ionospheric parameter on each satellite. The ionospheric delay time constraint model is constructed using a random walk model to build the ionospheric delay time constraint model of the above-mentioned narrow alley double-difference observation model; The narrow-lane Kalman filter is based on the ionospheric delay time constraint model of the narrow-lane double-difference observation model to filter the narrow-lane double-difference observation model and obtain the first floating-point position and the narrow-lane floating-point ambiguity. The wide-lane double-difference observation model consists of location parameters, double-difference wide-lane ambiguity, and relative zenith tropospheric delay. A wide-lane Kalman filter is used to filter the wide-lane double-difference observation model to obtain the second floating-point position and wide-lane floating-point ambiguity, and the wide-lane fixed ambiguity is obtained by LAMBDA search. The search module is used to obtain the fixed ambiguity of the wide lane using LAMBDA, and selects the wide lane fixed ambiguity with higher accuracy as the observation value to construct the constrained observation equation between the wide lane fixed ambiguity and the narrow lane floating-point ambiguity. in, To fix the ambiguity of the wide alley, and The floating-point ambiguities of the narrow lanes at frequencies L1 and L2 are respectively, and e is the rounding error of the floating-point ambiguity. The selection criterion for the wide lane fixed ambiguity with higher accuracy is that the difference between its fixed solution and the floating-point solution is less than 0.2 cycles. After the narrow lane floating-point ambiguity is constrained and corrected, at least a portion of the narrow lane fixed ambiguity is obtained by searching for fixed ambiguity. The ambiguity correctness check module is used to check the correctness of the fixed ambiguity of the narrow alleyway. The location update module is used to update the location parameters based on the fixed ambiguity of the narrow alley after verification, so as to obtain the real-time location coordinates.

9. The apparatus according to claim 8, characterized in that, The following three methods are combined to check the correctness of narrow alleyway ambiguity; Method 1: Perform an iterative chi-square test on the fixed single-frequency ambiguity residuals in the narrow alley fixed ambiguity and remove single-frequency ambiguities that do not conform to the chi-square distribution; Method 2: Combine the fixed dual-frequency ambiguities in the narrow alley fixed ambiguity and calculate to obtain the wide alley ambiguity, compare it with the fixed wide alley ambiguity, and remove inconsistent dual-frequency ambiguities; Method 3: Calculate the residuals v1 and v2 of the fixed dual-frequency ambiguities in the narrow alley fixed ambiguity, and filter the dual-frequency ambiguities according to the following conditions.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the medium-to-long baseline monitoring method based on the ionospheric delay estimation model as described in any one of claims 1 to 8.

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